Overview

Dataset statistics

Number of variables34
Number of observations720
Missing cells1433
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory191.4 KiB
Average record size in memory272.2 B

Variable types

Numeric7
Categorical27

Warnings

Corporate Sales Volume Actual has constant value "$0" Constant
Address has a high cardinality: 720 distinct values High cardinality
City has a high cardinality: 314 distinct values High cardinality
Neighborhood has a high cardinality: 347 distinct values High cardinality
Location Sales Volume Actual has a high cardinality: 342 distinct values High cardinality
Year Established is highly correlated with Years In DatabaseHigh correlation
Years In Database is highly correlated with Year EstablishedHigh correlation
Utilities Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
Location Sales Volume Range is highly correlated with Corporate Sales Volume Actual and 2 other fieldsHigh correlation
Metro Area is highly correlated with Corporate Sales Volume Actual and 2 other fieldsHigh correlation
Contract Labor Expenses is highly correlated with Insurance Expenses and 2 other fieldsHigh correlation
Management/Administration Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
Insurance Expenses is highly correlated with Contract Labor Expenses and 4 other fieldsHigh correlation
Rent Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
Computer Expenses is highly correlated with Contract Labor Expenses and 4 other fieldsHigh correlation
Advertising Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
Payroll and Benefits Expenses is highly correlated with Corporate Sales Volume Actual and 3 other fieldsHigh correlation
Location Employee Size Range is highly correlated with Corporate Sales Volume ActualHigh correlation
Corporate Sales Volume Actual is highly correlated with Utilities Expenses and 21 other fieldsHigh correlation
County is highly correlated with Metro Area and 2 other fieldsHigh correlation
Accounting Expenses is highly correlated with Payroll and Benefits Expenses and 1 other fieldsHigh correlation
Purchase Print Expenses is highly correlated with Location Sales Volume Range and 2 other fieldsHigh correlation
Company Name is highly correlated with Corporate Sales Volume Actual and 1 other fieldsHigh correlation
Legal Expenses is highly correlated with Insurance Expenses and 4 other fieldsHigh correlation
Package Container Expense is highly correlated with Corporate Sales Volume ActualHigh correlation
Office Supplies Expense is highly correlated with Location Sales Volume Range and 3 other fieldsHigh correlation
Telcom Expenses is highly correlated with Insurance Expenses and 4 other fieldsHigh correlation
Square Footage is highly correlated with Corporate Sales Volume ActualHigh correlation
State is highly correlated with Metro Area and 2 other fieldsHigh correlation
Own or Lease is highly correlated with Corporate Sales Volume ActualHigh correlation
Neighborhood has 294 (40.8%) missing values Missing
ZIP Four has 45 (6.2%) missing values Missing
Year Established has 700 (97.2%) missing values Missing
Own or Lease has 375 (52.1%) missing values Missing
Last Updated On is highly skewed (γ1 = -25.74226424) Skewed
df_index is uniformly distributed Uniform
Address is uniformly distributed Uniform
Neighborhood is uniformly distributed Uniform
df_index has unique values Unique
Address has unique values Unique

Reproduction

Analysis started2021-01-21 00:51:01.995104
Analysis finished2021-01-21 00:51:28.433187
Duration26.44 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct720
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.9861111
Minimum0
Maximum723
Zeros1
Zeros (%)0.1%
Memory size5.8 KiB
2021-01-21T00:51:28.644973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.95
Q1179.75
median359.5
Q3542.25
95-th percentile687.05
Maximum723
Range723
Interquartile range (IQR)362.5

Descriptive statistics

Standard deviation209.4338214
Coefficient of variation (CV)0.5801714108
Kurtosis-1.205064442
Mean360.9861111
Median Absolute Deviation (MAD)181.5
Skewness0.003660644368
Sum259910
Variance43862.52554
MonotocityStrictly increasing
2021-01-21T00:51:28.909220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7231
 
0.1%
2461
 
0.1%
2441
 
0.1%
2431
 
0.1%
2421
 
0.1%
2411
 
0.1%
2401
 
0.1%
2391
 
0.1%
2381
 
0.1%
2371
 
0.1%
Other values (710)710
98.6%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
ValueCountFrequency (%)
7231
0.1%
7221
0.1%
7211
0.1%
7201
0.1%
7191
0.1%

Advertising Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$50,000 to $100,000
469 
$20,000 to $50,000
190 
$100,000 to $250,000
 
45
$10,000 to $20,000
 
8
$5,000 to $10,000
 
7

Length

Max length20
Median length19
Mean length18.76773296
Min length17

Characters and Unicode

Total characters13494
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$20,000 to $50,000
2nd row$50,000 to $100,000
3rd row$50,000 to $100,000
4th row$50,000 to $100,000
5th row$50,000 to $100,000
ValueCountFrequency (%)
$50,000 to $100,000469
65.1%
$20,000 to $50,000190
26.4%
$100,000 to $250,00045
 
6.2%
$10,000 to $20,0008
 
1.1%
$5,000 to $10,0007
 
1.0%
(Missing)1
 
0.1%
2021-01-21T00:51:29.404607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:29.585162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to719
33.3%
50,000659
30.6%
100,000514
23.8%
20,000198
 
9.2%
250,00045
 
2.1%
10,00015
 
0.7%
5,0007
 
0.3%

Most occurring characters

ValueCountFrequency (%)
06259
46.4%
$1438
 
10.7%
,1438
 
10.7%
1438
 
10.7%
t719
 
5.3%
o719
 
5.3%
5711
 
5.3%
1529
 
3.9%
2243
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7742
57.4%
Currency Symbol1438
 
10.7%
Other Punctuation1438
 
10.7%
Space Separator1438
 
10.7%
Lowercase Letter1438
 
10.7%

Most frequent character per category

ValueCountFrequency (%)
06259
80.8%
5711
 
9.2%
1529
 
6.8%
2243
 
3.1%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%
ValueCountFrequency (%)
$1438
100.0%
ValueCountFrequency (%)
,1438
100.0%
ValueCountFrequency (%)
1438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12056
89.3%
Latin1438
 
10.7%

Most frequent character per script

ValueCountFrequency (%)
06259
51.9%
$1438
 
11.9%
,1438
 
11.9%
1438
 
11.9%
5711
 
5.9%
1529
 
4.4%
2243
 
2.0%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13494
100.0%

Most frequent character per block

ValueCountFrequency (%)
06259
46.4%
$1438
 
10.7%
,1438
 
10.7%
1438
 
10.7%
t719
 
5.3%
o719
 
5.3%
5711
 
5.3%
1529
 
3.9%
2243
 
1.8%

Accounting Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$5,000 to $10,000
486 
$2,500 to $5,000
128 
$10,000 to $25,000
88 
$1,000 to $2,500
 
10
$500 to $1,000
 
6

Length

Max length18
Median length17
Mean length16.90529248
Min length14

Characters and Unicode

Total characters12138
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,500 to $5,000
2nd row$5,000 to $10,000
3rd row$5,000 to $10,000
4th row$5,000 to $10,000
5th row$5,000 to $10,000
ValueCountFrequency (%)
$5,000 to $10,000486
67.5%
$2,500 to $5,000128
 
17.8%
$10,000 to $25,00088
 
12.2%
$1,000 to $2,50010
 
1.4%
$500 to $1,0006
 
0.8%
(Missing)2
 
0.3%
2021-01-21T00:51:30.355330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:30.510395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to718
33.3%
5,000614
28.5%
10,000574
26.6%
2,500138
 
6.4%
25,00088
 
4.1%
1,00016
 
0.7%
5006
 
0.3%

Most occurring characters

ValueCountFrequency (%)
04738
39.0%
$1436
 
11.8%
1436
 
11.8%
,1430
 
11.8%
5846
 
7.0%
t718
 
5.9%
o718
 
5.9%
1590
 
4.9%
2226
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6400
52.7%
Currency Symbol1436
 
11.8%
Space Separator1436
 
11.8%
Lowercase Letter1436
 
11.8%
Other Punctuation1430
 
11.8%

Most frequent character per category

ValueCountFrequency (%)
04738
74.0%
5846
 
13.2%
1590
 
9.2%
2226
 
3.5%
ValueCountFrequency (%)
t718
50.0%
o718
50.0%
ValueCountFrequency (%)
$1436
100.0%
ValueCountFrequency (%)
,1430
100.0%
ValueCountFrequency (%)
1436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10702
88.2%
Latin1436
 
11.8%

Most frequent character per script

ValueCountFrequency (%)
04738
44.3%
$1436
 
13.4%
1436
 
13.4%
,1430
 
13.4%
5846
 
7.9%
1590
 
5.5%
2226
 
2.1%
ValueCountFrequency (%)
t718
50.0%
o718
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12138
100.0%

Most frequent character per block

ValueCountFrequency (%)
04738
39.0%
$1436
 
11.8%
1436
 
11.8%
,1430
 
11.8%
5846
 
7.0%
t718
 
5.9%
o718
 
5.9%
1590
 
4.9%
2226
 
1.9%

Address
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct720
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
340 Route 25a
 
1
490 8th Ave
 
1
9180 Wiles Rd
 
1
101 E 170th St
 
1
621 Broadway
 
1
Other values (715)
715 

Length

Max length30
Median length16
Mean length16.525
Min length7

Characters and Unicode

Total characters11898
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique720 ?
Unique (%)100.0%

Sample

1st row265 W Oakland Park Blvd
2nd row326 Indian Trce
3rd row1020 Weston Rd
4th row9835 Okeechobee Blvd
5th row828 S Military Trl
ValueCountFrequency (%)
340 Route 25a1
 
0.1%
490 8th Ave1
 
0.1%
9180 Wiles Rd1
 
0.1%
101 E 170th St1
 
0.1%
621 Broadway1
 
0.1%
16601 Baisley Blvd1
 
0.1%
915 18th Ave1
 
0.1%
334 Montauk Hwy1
 
0.1%
753 Saint George Ave1
 
0.1%
485 Broadway1
 
0.1%
Other values (710)710
98.6%
2021-01-21T00:51:31.134505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ave184
 
7.0%
st134
 
5.1%
rd84
 
3.2%
blvd77
 
3.0%
w68
 
2.6%
s44
 
1.7%
route41
 
1.6%
hwy36
 
1.4%
state35
 
1.3%
34
 
1.3%
Other values (1067)1873
71.8%

Most occurring characters

ValueCountFrequency (%)
1890
 
15.9%
e665
 
5.6%
t578
 
4.9%
1577
 
4.8%
a473
 
4.0%
0428
 
3.6%
r354
 
3.0%
d340
 
2.9%
2325
 
2.7%
n325
 
2.7%
Other values (55)5943
49.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5184
43.6%
Decimal Number3064
25.8%
Space Separator1890
 
15.9%
Uppercase Letter1713
 
14.4%
Other Punctuation35
 
0.3%
Dash Punctuation12
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
S324
18.9%
A216
12.6%
R160
9.3%
W157
9.2%
B143
8.3%
H99
 
5.8%
N98
 
5.7%
M68
 
4.0%
C57
 
3.3%
P53
 
3.1%
Other values (16)338
19.7%
ValueCountFrequency (%)
e665
12.8%
t578
11.1%
a473
 
9.1%
r354
 
6.8%
d340
 
6.6%
n325
 
6.3%
o311
 
6.0%
l300
 
5.8%
i298
 
5.7%
v287
 
5.5%
Other values (15)1253
24.2%
ValueCountFrequency (%)
1577
18.8%
0428
14.0%
2325
10.6%
5311
10.2%
3308
10.1%
4254
8.3%
6235
7.7%
7218
 
7.1%
8216
 
7.0%
9192
 
6.3%
ValueCountFrequency (%)
#32
91.4%
&3
 
8.6%
ValueCountFrequency (%)
1890
100.0%
ValueCountFrequency (%)
-12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6897
58.0%
Common5001
42.0%

Most frequent character per script

ValueCountFrequency (%)
e665
 
9.6%
t578
 
8.4%
a473
 
6.9%
r354
 
5.1%
d340
 
4.9%
n325
 
4.7%
S324
 
4.7%
o311
 
4.5%
l300
 
4.3%
i298
 
4.3%
Other values (41)2929
42.5%
ValueCountFrequency (%)
1890
37.8%
1577
 
11.5%
0428
 
8.6%
2325
 
6.5%
5311
 
6.2%
3308
 
6.2%
4254
 
5.1%
6235
 
4.7%
7218
 
4.4%
8216
 
4.3%
Other values (4)239
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11898
100.0%

Most frequent character per block

ValueCountFrequency (%)
1890
 
15.9%
e665
 
5.6%
t578
 
4.9%
1577
 
4.8%
a473
 
4.0%
0428
 
3.6%
r354
 
3.0%
d340
 
2.9%
2325
 
2.7%
n325
 
2.7%
Other values (55)5943
49.9%

City
Categorical

HIGH CARDINALITY

Distinct314
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
New York
52 
Brooklyn
 
50
Miami
 
49
Bronx
 
38
Hialeah
 
16
Other values (309)
515 

Length

Max length22
Median length8
Mean length9.240277778
Min length5

Characters and Unicode

Total characters6653
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)31.0%

Sample

1st rowWilton Manors
2nd rowWeston
3rd rowWeston
4th rowWest Palm Beach
5th rowWest Palm Beach
ValueCountFrequency (%)
New York52
 
7.2%
Brooklyn50
 
6.9%
Miami49
 
6.8%
Bronx38
 
5.3%
Hialeah16
 
2.2%
Fort Lauderdale13
 
1.8%
West Palm Beach11
 
1.5%
Newark10
 
1.4%
Jamaica10
 
1.4%
Jersey City9
 
1.2%
Other values (304)462
64.2%
2021-01-21T00:51:31.637248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miami64
 
6.1%
new59
 
5.6%
york53
 
5.1%
brooklyn50
 
4.8%
beach48
 
4.6%
bronx38
 
3.6%
city20
 
1.9%
west18
 
1.7%
palm17
 
1.6%
north16
 
1.5%
Other values (322)664
63.4%

Most occurring characters

ValueCountFrequency (%)
a603
 
9.1%
e575
 
8.6%
o545
 
8.2%
r475
 
7.1%
i402
 
6.0%
n394
 
5.9%
l346
 
5.2%
327
 
4.9%
t280
 
4.2%
s218
 
3.3%
Other values (40)2488
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5279
79.3%
Uppercase Letter1047
 
15.7%
Space Separator327
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
a603
11.4%
e575
10.9%
o545
10.3%
r475
 
9.0%
i402
 
7.6%
n394
 
7.5%
l346
 
6.6%
t280
 
5.3%
s218
 
4.1%
k198
 
3.8%
Other values (15)1243
23.5%
ValueCountFrequency (%)
B193
18.4%
M100
9.6%
N94
 
9.0%
P79
 
7.5%
H67
 
6.4%
C60
 
5.7%
Y58
 
5.5%
S54
 
5.2%
L53
 
5.1%
R43
 
4.1%
Other values (14)246
23.5%
ValueCountFrequency (%)
327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6326
95.1%
Common327
 
4.9%

Most frequent character per script

ValueCountFrequency (%)
a603
 
9.5%
e575
 
9.1%
o545
 
8.6%
r475
 
7.5%
i402
 
6.4%
n394
 
6.2%
l346
 
5.5%
t280
 
4.4%
s218
 
3.4%
k198
 
3.1%
Other values (39)2290
36.2%
ValueCountFrequency (%)
327
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6653
100.0%

Most frequent character per block

ValueCountFrequency (%)
a603
 
9.1%
e575
 
8.6%
o545
 
8.2%
r475
 
7.1%
i402
 
6.0%
n394
 
5.9%
l346
 
5.2%
327
 
4.9%
t280
 
4.2%
s218
 
3.3%
Other values (40)2488
37.4%

State
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
NY
305 
FL
236 
NJ
177 
PA
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1440
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFL
2nd rowFL
3rd rowFL
4th rowFL
5th rowFL
ValueCountFrequency (%)
NY305
42.4%
FL236
32.8%
NJ177
24.6%
PA2
 
0.3%
2021-01-21T00:51:32.080333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:32.217340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
ny305
42.4%
fl236
32.8%
nj177
24.6%
pa2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N482
33.5%
Y305
21.2%
F236
16.4%
L236
16.4%
J177
 
12.3%
P2
 
0.1%
A2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1440
100.0%

Most frequent character per category

ValueCountFrequency (%)
N482
33.5%
Y305
21.2%
F236
16.4%
L236
16.4%
J177
 
12.3%
P2
 
0.1%
A2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1440
100.0%

Most frequent character per script

ValueCountFrequency (%)
N482
33.5%
Y305
21.2%
F236
16.4%
L236
16.4%
J177
 
12.3%
P2
 
0.1%
A2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1440
100.0%

Most frequent character per block

ValueCountFrequency (%)
N482
33.5%
Y305
21.2%
F236
16.4%
L236
16.4%
J177
 
12.3%
P2
 
0.1%
A2
 
0.1%

County
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Miami Dade
104 
Broward
80 
New York
52 
Palm Beach
52 
Kings
50 
Other values (21)
382 

Length

Max length11
Median length7
Mean length7.366666667
Min length4

Characters and Unicode

Total characters5304
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBroward
2nd rowBroward
3rd rowBroward
4th rowPalm Beach
5th rowPalm Beach
ValueCountFrequency (%)
Miami Dade104
14.4%
Broward80
11.1%
New York52
 
7.2%
Palm Beach52
 
7.2%
Kings50
 
6.9%
Queens47
 
6.5%
Suffolk46
 
6.4%
Bronx38
 
5.3%
Nassau35
 
4.9%
Middlesex23
 
3.2%
Other values (16)193
26.8%
2021-01-21T00:51:32.672431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dade104
 
11.2%
miami104
 
11.2%
broward80
 
8.6%
new52
 
5.6%
palm52
 
5.6%
york52
 
5.6%
beach52
 
5.6%
kings50
 
5.4%
queens47
 
5.1%
suffolk46
 
5.0%
Other values (19)289
31.1%

Most occurring characters

ValueCountFrequency (%)
a510
 
9.6%
e508
 
9.6%
s338
 
6.4%
o335
 
6.3%
r334
 
6.3%
i334
 
6.3%
n269
 
5.1%
d264
 
5.0%
208
 
3.9%
m195
 
3.7%
Other values (25)2009
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4168
78.6%
Uppercase Letter928
 
17.5%
Space Separator208
 
3.9%

Most frequent character per category

ValueCountFrequency (%)
a510
12.2%
e508
12.2%
s338
 
8.1%
o335
 
8.0%
r334
 
8.0%
i334
 
8.0%
n269
 
6.5%
d264
 
6.3%
m195
 
4.7%
u177
 
4.2%
Other values (9)904
21.7%
ValueCountFrequency (%)
B189
20.4%
M166
17.9%
D104
11.2%
N87
9.4%
P67
 
7.2%
S57
 
6.1%
Y52
 
5.6%
K50
 
5.4%
Q47
 
5.1%
H20
 
2.2%
Other values (5)89
9.6%
ValueCountFrequency (%)
208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5096
96.1%
Common208
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
a510
 
10.0%
e508
 
10.0%
s338
 
6.6%
o335
 
6.6%
r334
 
6.6%
i334
 
6.6%
n269
 
5.3%
d264
 
5.2%
m195
 
3.8%
B189
 
3.7%
Other values (24)1820
35.7%
ValueCountFrequency (%)
208
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5304
100.0%

Most frequent character per block

ValueCountFrequency (%)
a510
 
9.6%
e508
 
9.6%
s338
 
6.4%
o335
 
6.3%
r334
 
6.3%
i334
 
6.3%
n269
 
5.1%
d264
 
5.0%
208
 
3.9%
m195
 
3.7%
Other values (25)2009
37.9%

Metro Area
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Nw Yrk, NY-NJ-PA
484 
Miami-Ft Ldr, FL
236 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters11520
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiami-Ft Ldr, FL
2nd rowMiami-Ft Ldr, FL
3rd rowMiami-Ft Ldr, FL
4th rowMiami-Ft Ldr, FL
5th rowMiami-Ft Ldr, FL
ValueCountFrequency (%)
Nw Yrk, NY-NJ-PA484
67.2%
Miami-Ft Ldr, FL236
32.8%
2021-01-21T00:51:33.090739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:33.234441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
ny-nj-pa484
22.4%
yrk484
22.4%
nw484
22.4%
ldr236
10.9%
fl236
10.9%
miami-ft236
10.9%

Most occurring characters

ValueCountFrequency (%)
N1452
12.6%
1440
12.5%
-1204
 
10.5%
Y968
 
8.4%
r720
 
6.2%
,720
 
6.2%
w484
 
4.2%
k484
 
4.2%
J484
 
4.2%
P484
 
4.2%
Other values (9)3080
26.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5052
43.9%
Lowercase Letter3104
26.9%
Space Separator1440
 
12.5%
Dash Punctuation1204
 
10.5%
Other Punctuation720
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
N1452
28.7%
Y968
19.2%
J484
 
9.6%
P484
 
9.6%
A484
 
9.6%
F472
 
9.3%
L472
 
9.3%
M236
 
4.7%
ValueCountFrequency (%)
r720
23.2%
w484
15.6%
k484
15.6%
i472
15.2%
a236
 
7.6%
m236
 
7.6%
t236
 
7.6%
d236
 
7.6%
ValueCountFrequency (%)
-1204
100.0%
ValueCountFrequency (%)
1440
100.0%
ValueCountFrequency (%)
,720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8156
70.8%
Common3364
29.2%

Most frequent character per script

ValueCountFrequency (%)
N1452
17.8%
Y968
11.9%
r720
8.8%
w484
 
5.9%
k484
 
5.9%
J484
 
5.9%
P484
 
5.9%
A484
 
5.9%
i472
 
5.8%
F472
 
5.8%
Other values (6)1652
20.3%
ValueCountFrequency (%)
1440
42.8%
-1204
35.8%
,720
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11520
100.0%

Most frequent character per block

ValueCountFrequency (%)
N1452
12.6%
1440
12.5%
-1204
 
10.5%
Y968
 
8.4%
r720
 
6.2%
,720
 
6.2%
w484
 
4.2%
k484
 
4.2%
J484
 
4.2%
P484
 
4.2%
Other values (9)3080
26.7%

Neighborhood
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct347
Distinct (%)81.5%
Missing294
Missing (%)40.8%
Memory size5.8 KiB
Theater District
 
5
Garment District
 
4
Midtown East
 
4
East Harlem
 
4
Central Harlem
 
4
Other values (342)
405 

Length

Max length39
Median length12
Mean length12.62206573
Min length4

Characters and Unicode

Total characters5377
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique289 ?
Unique (%)67.8%

Sample

1st rowSleepy River
2nd rowWeston
3rd rowBaywinds
4th rowGolden Lakes
5th rowShoppes At Cresthaven
ValueCountFrequency (%)
Theater District5
 
0.7%
Garment District4
 
0.6%
Midtown East4
 
0.6%
East Harlem4
 
0.6%
Central Harlem4
 
0.6%
Astoria3
 
0.4%
Cutler Bay3
 
0.4%
Jamaica3
 
0.4%
Canarsie3
 
0.4%
East New York3
 
0.4%
Other values (337)390
54.2%
(Missing)294
40.8%
2021-01-21T00:51:33.739140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east29
 
3.7%
park24
 
3.0%
heights21
 
2.7%
west19
 
2.4%
north15
 
1.9%
bay14
 
1.8%
district14
 
1.8%
south13
 
1.6%
miami13
 
1.6%
center11
 
1.4%
Other values (370)615
78.0%

Most occurring characters

ValueCountFrequency (%)
e499
 
9.3%
a427
 
7.9%
t366
 
6.8%
r364
 
6.8%
362
 
6.7%
o342
 
6.4%
i320
 
6.0%
n296
 
5.5%
s285
 
5.3%
l253
 
4.7%
Other values (44)1863
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4206
78.2%
Uppercase Letter794
 
14.8%
Space Separator362
 
6.7%
Dash Punctuation8
 
0.1%
Decimal Number6
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e499
11.9%
a427
10.2%
t366
8.7%
r364
8.7%
o342
 
8.1%
i320
 
7.6%
n296
 
7.0%
s285
 
6.8%
l253
 
6.0%
h146
 
3.5%
Other values (15)908
21.6%
ValueCountFrequency (%)
C92
 
11.6%
S72
 
9.1%
B63
 
7.9%
H61
 
7.7%
P59
 
7.4%
W46
 
5.8%
E44
 
5.5%
M43
 
5.4%
G40
 
5.0%
F35
 
4.4%
Other values (14)239
30.1%
ValueCountFrequency (%)
44
66.7%
12
33.3%
ValueCountFrequency (%)
362
100.0%
ValueCountFrequency (%)
-8
100.0%
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5000
93.0%
Common377
 
7.0%

Most frequent character per script

ValueCountFrequency (%)
e499
 
10.0%
a427
 
8.5%
t366
 
7.3%
r364
 
7.3%
o342
 
6.8%
i320
 
6.4%
n296
 
5.9%
s285
 
5.7%
l253
 
5.1%
h146
 
2.9%
Other values (39)1702
34.0%
ValueCountFrequency (%)
362
96.0%
-8
 
2.1%
44
 
1.1%
12
 
0.5%
.1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5377
100.0%

Most frequent character per block

ValueCountFrequency (%)
e499
 
9.3%
a427
 
7.9%
t366
 
6.8%
r364
 
6.8%
362
 
6.7%
o342
 
6.4%
i320
 
6.0%
n296
 
5.5%
s285
 
5.3%
l253
 
4.7%
Other values (44)1863
34.6%

ZIP Code
Real number (ℝ≥0)

Distinct505
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17503.52361
Minimum7001
Maximum33498
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-21T00:51:33.995183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7001
5-th percentile7092.7
Q110001
median11236
Q333067.25
95-th percentile33414
Maximum33498
Range26497
Interquartile range (IQR)23066.25

Descriptive statistics

Standard deviation11076.54505
Coefficient of variation (CV)0.6328180142
Kurtosis-1.464297706
Mean17503.52361
Median Absolute Deviation (MAD)3364.5
Skewness0.6806313806
Sum12602537
Variance122689850.3
MonotocityNot monotonic
2021-01-21T00:51:34.239149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100015
 
0.7%
330125
 
0.7%
100364
 
0.6%
331264
 
0.6%
333514
 
0.6%
330154
 
0.6%
87534
 
0.6%
331574
 
0.6%
334144
 
0.6%
334114
 
0.6%
Other values (495)678
94.2%
ValueCountFrequency (%)
70011
0.1%
70022
0.3%
70032
0.3%
70041
0.1%
70051
0.1%
ValueCountFrequency (%)
334981
0.1%
334961
0.1%
334861
0.1%
334841
0.1%
334831
0.1%

ZIP Four
Real number (ℝ≥0)

MISSING

Distinct610
Distinct (%)90.4%
Missing45
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean3678.278519
Minimum1001
Maximum9847
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-21T00:51:34.701085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1207.4
Q12109.5
median3320
Q34918
95-th percentile7200.9
Maximum9847
Range8846
Interquartile range (IQR)2808.5

Descriptive statistics

Standard deviation1901.685236
Coefficient of variation (CV)0.5170041439
Kurtosis0.0897877977
Mean3678.278519
Median Absolute Deviation (MAD)1319
Skewness0.7539090636
Sum2482838
Variance3616406.738
MonotocityNot monotonic
2021-01-21T00:51:34.950147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60023
 
0.4%
44013
 
0.4%
21013
 
0.4%
20113
 
0.4%
13023
 
0.4%
32013
 
0.4%
19062
 
0.3%
28272
 
0.3%
31572
 
0.3%
15292
 
0.3%
Other values (600)649
90.1%
(Missing)45
 
6.2%
ValueCountFrequency (%)
10011
0.1%
10031
0.1%
10042
0.3%
10061
0.1%
10101
0.1%
ValueCountFrequency (%)
98471
0.1%
97841
0.1%
95481
0.1%
95011
0.1%
94941
0.1%

Year Established
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)70.0%
Missing700
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean2003.25
Minimum1975
Maximum2017
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-21T00:51:35.168369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1975
5-th percentile1988.3
Q11998.75
median2003.5
Q32009.75
95-th percentile2016.05
Maximum2017
Range42
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.64189833
Coefficient of variation (CV)0.005312316649
Kurtosis1.248570215
Mean2003.25
Median Absolute Deviation (MAD)5
Skewness-0.9014156149
Sum40065
Variance113.25
MonotocityNot monotonic
2021-01-21T00:51:35.333253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
19892
 
0.3%
20032
 
0.3%
20082
 
0.3%
20152
 
0.3%
20042
 
0.3%
20162
 
0.3%
20071
 
0.1%
19981
 
0.1%
20171
 
0.1%
19991
 
0.1%
Other values (4)4
 
0.6%
(Missing)700
97.2%
ValueCountFrequency (%)
19751
0.1%
19892
0.3%
19971
0.1%
19981
0.1%
19991
0.1%
ValueCountFrequency (%)
20171
0.1%
20162
0.3%
20152
0.3%
20082
0.3%
20071
0.1%

Years In Database
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.53472222
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-21T00:51:35.531153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q116
median26
Q335
95-th percentile37
Maximum37
Range36
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.0743503
Coefficient of variation (CV)0.4106160327
Kurtosis-1.089035403
Mean24.53472222
Median Absolute Deviation (MAD)10
Skewness-0.2915507238
Sum17665
Variance101.492534
MonotocityNot monotonic
2021-01-21T00:51:35.760469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
37152
21.1%
1242
 
5.8%
2839
 
5.4%
2430
 
4.2%
1328
 
3.9%
1426
 
3.6%
3625
 
3.5%
2524
 
3.3%
2624
 
3.3%
2723
 
3.2%
Other values (27)307
42.6%
ValueCountFrequency (%)
14
0.6%
23
0.4%
31
 
0.1%
45
0.7%
55
0.7%
ValueCountFrequency (%)
37152
21.1%
3625
 
3.5%
3510
 
1.4%
3414
 
1.9%
338
 
1.1%

Company Name
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Mc Donald's
712 
Mc Donald's Corporate Office
 
7
Mc Donald's Bbq
 
1

Length

Max length28
Median length11
Mean length11.17083333
Min length11

Characters and Unicode

Total characters8043
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowMc Donald's
2nd rowMc Donald's
3rd rowMc Donald's
4th rowMc Donald's
5th rowMc Donald's
ValueCountFrequency (%)
Mc Donald's712
98.9%
Mc Donald's Corporate Office7
 
1.0%
Mc Donald's Bbq1
 
0.1%
2021-01-21T00:51:36.237959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:36.383398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
mc720
49.5%
donald's720
49.5%
corporate7
 
0.5%
office7
 
0.5%
bbq1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
735
9.1%
o734
9.1%
c727
9.0%
a727
9.0%
M720
9.0%
D720
9.0%
n720
9.0%
l720
9.0%
d720
9.0%
'720
9.0%
Other values (12)800
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5133
63.8%
Uppercase Letter1455
 
18.1%
Space Separator735
 
9.1%
Other Punctuation720
 
9.0%

Most frequent character per category

ValueCountFrequency (%)
o734
14.3%
c727
14.2%
a727
14.2%
n720
14.0%
l720
14.0%
d720
14.0%
s720
14.0%
r14
 
0.3%
e14
 
0.3%
f14
 
0.3%
Other values (5)23
 
0.4%
ValueCountFrequency (%)
M720
49.5%
D720
49.5%
C7
 
0.5%
O7
 
0.5%
B1
 
0.1%
ValueCountFrequency (%)
735
100.0%
ValueCountFrequency (%)
'720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6588
81.9%
Common1455
 
18.1%

Most frequent character per script

ValueCountFrequency (%)
o734
11.1%
c727
11.0%
a727
11.0%
M720
10.9%
D720
10.9%
n720
10.9%
l720
10.9%
d720
10.9%
s720
10.9%
r14
 
0.2%
Other values (10)66
 
1.0%
ValueCountFrequency (%)
735
50.5%
'720
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8043
100.0%

Most frequent character per block

ValueCountFrequency (%)
735
9.1%
o734
9.1%
c727
9.0%
a727
9.0%
M720
9.0%
D720
9.0%
n720
9.0%
l720
9.0%
d720
9.0%
'720
9.0%
Other values (12)800
9.9%

Computer Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$2,500 to $5,000
481 
$5,000 to $10,000
136 
$1,000 to $2,500
90 
Less than $500
 
6
$500 to $1,000
 
6

Length

Max length17
Median length16
Mean length16.15577191
Min length14

Characters and Unicode

Total characters11616
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $2,500
2nd row$2,500 to $5,000
3rd row$2,500 to $5,000
4th row$2,500 to $5,000
5th row$2,500 to $5,000
ValueCountFrequency (%)
$2,500 to $5,000481
66.8%
$5,000 to $10,000136
 
18.9%
$1,000 to $2,50090
 
12.5%
Less than $5006
 
0.8%
$500 to $1,0006
 
0.8%
(Missing)1
 
0.1%
2021-01-21T00:51:36.758749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:36.908335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to713
33.1%
5,000617
28.6%
2,500571
26.5%
10,000136
 
6.3%
1,00096
 
4.5%
50012
 
0.6%
less6
 
0.3%
than6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
03849
33.1%
1438
 
12.4%
$1432
 
12.3%
,1420
 
12.2%
51200
 
10.3%
t719
 
6.2%
o713
 
6.1%
2571
 
4.9%
1232
 
2.0%
s12
 
0.1%
Other values (5)30
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5852
50.4%
Lowercase Letter1468
 
12.6%
Space Separator1438
 
12.4%
Currency Symbol1432
 
12.3%
Other Punctuation1420
 
12.2%
Uppercase Letter6
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t719
49.0%
o713
48.6%
s12
 
0.8%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
03849
65.8%
51200
 
20.5%
2571
 
9.8%
1232
 
4.0%
ValueCountFrequency (%)
$1432
100.0%
ValueCountFrequency (%)
,1420
100.0%
ValueCountFrequency (%)
1438
100.0%
ValueCountFrequency (%)
L6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10142
87.3%
Latin1474
 
12.7%

Most frequent character per script

ValueCountFrequency (%)
t719
48.8%
o713
48.4%
s12
 
0.8%
L6
 
0.4%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
03849
38.0%
1438
 
14.2%
$1432
 
14.1%
,1420
 
14.0%
51200
 
11.8%
2571
 
5.6%
1232
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11616
100.0%

Most frequent character per block

ValueCountFrequency (%)
03849
33.1%
1438
 
12.4%
$1432
 
12.3%
,1420
 
12.2%
51200
 
10.3%
t719
 
6.2%
o713
 
6.1%
2571
 
4.9%
1232
 
2.0%
s12
 
0.1%
Other values (5)30
 
0.3%

Contract Labor Expenses
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$1,000 to $10,000
536 
$10,000 to $50,000
177 
Less than $1,000
 
6

Length

Max length18
Median length17
Mean length17.23783032
Min length16

Characters and Unicode

Total characters12394
Distinct characters14
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $10,000
2nd row$1,000 to $10,000
3rd row$1,000 to $10,000
4th row$1,000 to $10,000
5th row$1,000 to $10,000
ValueCountFrequency (%)
$1,000 to $10,000536
74.4%
$10,000 to $50,000177
 
24.6%
Less than $1,0006
 
0.8%
(Missing)1
 
0.1%
2021-01-21T00:51:37.435622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:37.599599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
10,000713
33.1%
to713
33.1%
1,000542
25.1%
50,000177
 
8.2%
less6
 
0.3%
than6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
05186
41.8%
1438
 
11.6%
$1432
 
11.6%
,1432
 
11.6%
11255
 
10.1%
t719
 
5.8%
o713
 
5.8%
5177
 
1.4%
s12
 
0.1%
L6
 
< 0.1%
Other values (4)24
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6618
53.4%
Lowercase Letter1468
 
11.8%
Space Separator1438
 
11.6%
Currency Symbol1432
 
11.6%
Other Punctuation1432
 
11.6%
Uppercase Letter6
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t719
49.0%
o713
48.6%
s12
 
0.8%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
05186
78.4%
11255
 
19.0%
5177
 
2.7%
ValueCountFrequency (%)
$1432
100.0%
ValueCountFrequency (%)
,1432
100.0%
ValueCountFrequency (%)
1438
100.0%
ValueCountFrequency (%)
L6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10920
88.1%
Latin1474
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
t719
48.8%
o713
48.4%
s12
 
0.8%
L6
 
0.4%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
05186
47.5%
1438
 
13.2%
$1432
 
13.1%
,1432
 
13.1%
11255
 
11.5%
5177
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII12394
100.0%

Most frequent character per block

ValueCountFrequency (%)
05186
41.8%
1438
 
11.6%
$1432
 
11.6%
,1432
 
11.6%
11255
 
10.1%
t719
 
5.8%
o713
 
5.8%
5177
 
1.4%
s12
 
0.1%
L6
 
< 0.1%
Other values (4)24
 
0.2%

Corporate Sales Volume Actual
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
$0
720 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1440
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$0
2nd row$0
3rd row$0
4th row$0
5th row$0
ValueCountFrequency (%)
$0720
100.0%
2021-01-21T00:51:37.999251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:38.125695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0720
100.0%

Most occurring characters

ValueCountFrequency (%)
$720
50.0%
0720
50.0%

Most occurring categories

ValueCountFrequency (%)
Currency Symbol720
50.0%
Decimal Number720
50.0%

Most frequent character per category

ValueCountFrequency (%)
$720
100.0%
ValueCountFrequency (%)
0720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1440
100.0%

Most frequent character per script

ValueCountFrequency (%)
$720
50.0%
0720
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1440
100.0%

Most frequent character per block

ValueCountFrequency (%)
$720
50.0%
0720
50.0%

Insurance Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$25,000 to $50,000
478 
$50,000 to $100,000
141 
$10,000 to $25,000
88 
$5,000 to $10,000
 
6
$2,500 to $5,000
 
6

Length

Max length19
Median length18
Mean length18.17107093
Min length16

Characters and Unicode

Total characters13065
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$25,000 to $50,000
3rd row$25,000 to $50,000
4th row$25,000 to $50,000
5th row$25,000 to $50,000
ValueCountFrequency (%)
$25,000 to $50,000478
66.4%
$50,000 to $100,000141
 
19.6%
$10,000 to $25,00088
 
12.2%
$5,000 to $10,0006
 
0.8%
$2,500 to $5,0006
 
0.8%
(Missing)1
 
0.1%
2021-01-21T00:51:38.480369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:38.624379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to719
33.3%
50,000619
28.7%
25,000566
26.2%
100,000141
 
6.5%
10,00094
 
4.4%
5,00012
 
0.6%
2,5006
 
0.3%

Most occurring characters

ValueCountFrequency (%)
05303
40.6%
$1438
 
11.0%
,1438
 
11.0%
1438
 
11.0%
51203
 
9.2%
t719
 
5.5%
o719
 
5.5%
2572
 
4.4%
1235
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7313
56.0%
Currency Symbol1438
 
11.0%
Other Punctuation1438
 
11.0%
Space Separator1438
 
11.0%
Lowercase Letter1438
 
11.0%

Most frequent character per category

ValueCountFrequency (%)
05303
72.5%
51203
 
16.5%
2572
 
7.8%
1235
 
3.2%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%
ValueCountFrequency (%)
$1438
100.0%
ValueCountFrequency (%)
,1438
100.0%
ValueCountFrequency (%)
1438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11627
89.0%
Latin1438
 
11.0%

Most frequent character per script

ValueCountFrequency (%)
05303
45.6%
$1438
 
12.4%
,1438
 
12.4%
1438
 
12.4%
51203
 
10.3%
2572
 
4.9%
1235
 
2.0%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13065
100.0%

Most frequent character per block

ValueCountFrequency (%)
05303
40.6%
$1438
 
11.0%
,1438
 
11.0%
1438
 
11.0%
51203
 
9.2%
t719
 
5.5%
o719
 
5.5%
2572
 
4.4%
1235
 
1.8%

Last Updated On
Real number (ℝ≥0)

SKEWED

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202009.85
Minimum201910
Maximum202012
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-21T00:51:38.937857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum201910
5-th percentile202010
Q1202010
median202010
Q3202010
95-th percentile202010
Maximum202012
Range102
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.77934508
Coefficient of variation (CV)1.870871683 × 105
Kurtosis680.3438892
Mean202009.85
Median Absolute Deviation (MAD)0
Skewness-25.74226424
Sum145447092
Variance14.28344924
MonotocityNot monotonic
2021-01-21T00:51:39.118396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
202010674
93.6%
20201121
 
2.9%
20201211
 
1.5%
2020067
 
1.0%
2020075
 
0.7%
2020021
 
0.1%
2019101
 
0.1%
ValueCountFrequency (%)
2019101
 
0.1%
2020021
 
0.1%
2020067
 
1.0%
2020075
 
0.7%
202010674
93.6%
ValueCountFrequency (%)
20201211
 
1.5%
20201121
 
2.9%
202010674
93.6%
2020075
 
0.7%
2020067
 
1.0%

Legal Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$2,500 to $5,000
485 
$5,000 to $10,000
117 
$1,000 to $2,500
104 
Less than $500
 
6
$500 to $1,000
 
6

Length

Max length17
Median length16
Mean length16.12952646
Min length14

Characters and Unicode

Total characters11581
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $2,500
2nd row$2,500 to $5,000
3rd row$2,500 to $5,000
4th row$2,500 to $5,000
5th row$2,500 to $5,000
ValueCountFrequency (%)
$2,500 to $5,000485
67.4%
$5,000 to $10,000117
 
16.2%
$1,000 to $2,500104
 
14.4%
Less than $5006
 
0.8%
$500 to $1,0006
 
0.8%
(Missing)2
 
0.3%
2021-01-21T00:51:39.561636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:39.714449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to712
33.1%
5,000602
27.9%
2,500589
27.3%
10,000117
 
5.4%
1,000110
 
5.1%
50012
 
0.6%
less6
 
0.3%
than6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
03806
32.9%
1436
 
12.4%
$1430
 
12.3%
,1418
 
12.2%
51203
 
10.4%
t718
 
6.2%
o712
 
6.1%
2589
 
5.1%
1227
 
2.0%
s12
 
0.1%
Other values (5)30
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5825
50.3%
Lowercase Letter1466
 
12.7%
Space Separator1436
 
12.4%
Currency Symbol1430
 
12.3%
Other Punctuation1418
 
12.2%
Uppercase Letter6
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t718
49.0%
o712
48.6%
s12
 
0.8%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
03806
65.3%
51203
 
20.7%
2589
 
10.1%
1227
 
3.9%
ValueCountFrequency (%)
$1430
100.0%
ValueCountFrequency (%)
,1418
100.0%
ValueCountFrequency (%)
1436
100.0%
ValueCountFrequency (%)
L6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10109
87.3%
Latin1472
 
12.7%

Most frequent character per script

ValueCountFrequency (%)
t718
48.8%
o712
48.4%
s12
 
0.8%
L6
 
0.4%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
03806
37.6%
1436
 
14.2%
$1430
 
14.1%
,1418
 
14.0%
51203
 
11.9%
2589
 
5.8%
1227
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11581
100.0%

Most frequent character per block

ValueCountFrequency (%)
03806
32.9%
1436
 
12.4%
$1430
 
12.3%
,1418
 
12.2%
51203
 
10.4%
t718
 
6.2%
o712
 
6.1%
2589
 
5.1%
1227
 
2.0%
s12
 
0.1%
Other values (5)30
 
0.3%

Location Employee Size Actual
Real number (ℝ≥0)

Distinct72
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.96666667
Minimum0
Maximum175
Zeros1
Zeros (%)0.1%
Memory size5.8 KiB
2021-01-21T00:51:40.047250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q137
median45
Q354
95-th percentile75
Maximum175
Range175
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.67769958
Coefficient of variation (CV)0.341066706
Kurtosis6.561316852
Mean45.96666667
Median Absolute Deviation (MAD)8.5
Skewness0.9450867545
Sum33096
Variance245.7902643
MonotocityNot monotonic
2021-01-21T00:51:40.300804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45151
21.0%
5063
 
8.8%
4044
 
6.1%
6042
 
5.8%
3034
 
4.7%
3532
 
4.4%
4222
 
3.1%
6521
 
2.9%
5520
 
2.8%
2516
 
2.2%
Other values (62)275
38.2%
ValueCountFrequency (%)
01
 
0.1%
42
 
0.3%
55
0.7%
72
 
0.3%
81
 
0.1%
ValueCountFrequency (%)
1751
 
0.1%
1002
0.3%
901
 
0.1%
861
 
0.1%
854
0.6%

Location Employee Size Range
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.8%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
20 to 49
435 
50 to 99
260 
10 to 19
 
11
5 to 9
 
8
100 to 249
 
3

Length

Max length10
Median length8
Mean length7.980528512
Min length6

Characters and Unicode

Total characters5738
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20 to 49
2nd row20 to 49
3rd row50 to 99
4th row20 to 49
5th row50 to 99
ValueCountFrequency (%)
20 to 49435
60.4%
50 to 99260
36.1%
10 to 1911
 
1.5%
5 to 98
 
1.1%
100 to 2493
 
0.4%
1 to 42
 
0.3%
(Missing)1
 
0.1%
2021-01-21T00:51:40.774598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:40.936738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to719
33.3%
20435
20.2%
49435
20.2%
50260
 
12.1%
99260
 
12.1%
1911
 
0.5%
1011
 
0.5%
98
 
0.4%
58
 
0.4%
1003
 
0.1%
Other values (3)7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1438
25.1%
9977
17.0%
t719
12.5%
o719
12.5%
0712
12.4%
4440
 
7.7%
2438
 
7.6%
5268
 
4.7%
127
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2862
49.9%
Space Separator1438
25.1%
Lowercase Letter1438
25.1%

Most frequent character per category

ValueCountFrequency (%)
9977
34.1%
0712
24.9%
4440
15.4%
2438
15.3%
5268
 
9.4%
127
 
0.9%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%
ValueCountFrequency (%)
1438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4300
74.9%
Latin1438
 
25.1%

Most frequent character per script

ValueCountFrequency (%)
1438
33.4%
9977
22.7%
0712
16.6%
4440
 
10.2%
2438
 
10.2%
5268
 
6.2%
127
 
0.6%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5738
100.0%

Most frequent character per block

ValueCountFrequency (%)
1438
25.1%
9977
17.0%
t719
12.5%
o719
12.5%
0712
12.4%
4440
 
7.7%
2438
 
7.6%
5268
 
4.7%
127
 
0.5%

Location Sales Volume Actual
Categorical

HIGH CARDINALITY

Distinct342
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
$4,188,000
 
42
$2,337,000
 
21
$2,345,000
 
16
$3,713,000
 
14
$2,134,000
 
12
Other values (337)
615 

Length

Max length10
Median length10
Mean length9.930555556
Min length2

Characters and Unicode

Total characters7150
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)29.2%

Sample

1st row$1,564,000
2nd row$2,554,000
3rd row$2,606,000
4th row$2,372,000
5th row$2,952,000
ValueCountFrequency (%)
$4,188,00042
 
5.8%
$2,337,00021
 
2.9%
$2,345,00016
 
2.2%
$3,713,00014
 
1.9%
$2,134,00012
 
1.7%
$2,372,00011
 
1.5%
$2,606,00011
 
1.5%
$2,031,00010
 
1.4%
$2,091,0009
 
1.2%
$2,182,0008
 
1.1%
Other values (332)566
78.6%
2021-01-21T00:51:41.466347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,188,00042
 
5.8%
2,337,00021
 
2.9%
2,345,00016
 
2.2%
3,713,00014
 
1.9%
2,134,00012
 
1.7%
2,606,00011
 
1.5%
2,372,00011
 
1.5%
2,031,00010
 
1.4%
2,091,0009
 
1.2%
2,182,0008
 
1.1%
Other values (332)566
78.6%

Most occurring characters

ValueCountFrequency (%)
02332
32.6%
,1417
19.8%
$720
 
10.1%
2546
 
7.6%
1413
 
5.8%
3373
 
5.2%
8292
 
4.1%
4256
 
3.6%
6223
 
3.1%
7211
 
3.0%
Other values (2)367
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5013
70.1%
Other Punctuation1417
 
19.8%
Currency Symbol720
 
10.1%

Most frequent character per category

ValueCountFrequency (%)
02332
46.5%
2546
 
10.9%
1413
 
8.2%
3373
 
7.4%
8292
 
5.8%
4256
 
5.1%
6223
 
4.4%
7211
 
4.2%
5200
 
4.0%
9167
 
3.3%
ValueCountFrequency (%)
$720
100.0%
ValueCountFrequency (%)
,1417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7150
100.0%

Most frequent character per script

ValueCountFrequency (%)
02332
32.6%
,1417
19.8%
$720
 
10.1%
2546
 
7.6%
1413
 
5.8%
3373
 
5.2%
8292
 
4.1%
4256
 
3.6%
6223
 
3.1%
7211
 
3.0%
Other values (2)367
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7150
100.0%

Most frequent character per block

ValueCountFrequency (%)
02332
32.6%
,1417
19.8%
$720
 
10.1%
2546
 
7.6%
1413
 
5.8%
3373
 
5.2%
8292
 
4.1%
4256
 
3.6%
6223
 
3.1%
7211
 
3.0%
Other values (2)367
 
5.1%

Location Sales Volume Range
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$1-2.5 Million
370 
$2.5-5 Million
328 
$500,000-1 Million
 
13
Less Than $500,000
 
8

Length

Max length18
Median length14
Mean length14.11682893
Min length14

Characters and Unicode

Total characters10150
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1-2.5 Million
2nd row$2.5-5 Million
3rd row$2.5-5 Million
4th row$1-2.5 Million
5th row$2.5-5 Million
ValueCountFrequency (%)
$1-2.5 Million370
51.4%
$2.5-5 Million328
45.6%
$500,000-1 Million13
 
1.8%
Less Than $500,0008
 
1.1%
(Missing)1
 
0.1%
2021-01-21T00:51:41.932786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:42.091172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
million711
49.2%
1-2.5370
25.6%
2.5-5328
22.7%
500,000-113
 
0.9%
less8
 
0.6%
than8
 
0.6%
500,0008
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i1422
14.0%
l1422
14.0%
51047
10.3%
727
7.2%
$719
7.1%
n719
7.1%
-711
7.0%
M711
7.0%
o711
7.0%
2698
6.9%
Other values (10)1263
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4314
42.5%
Decimal Number2233
22.0%
Space Separator727
 
7.2%
Uppercase Letter727
 
7.2%
Currency Symbol719
 
7.1%
Other Punctuation719
 
7.1%
Dash Punctuation711
 
7.0%

Most frequent character per category

ValueCountFrequency (%)
i1422
33.0%
l1422
33.0%
n719
16.7%
o711
16.5%
s16
 
0.4%
e8
 
0.2%
h8
 
0.2%
a8
 
0.2%
ValueCountFrequency (%)
51047
46.9%
2698
31.3%
1383
 
17.2%
0105
 
4.7%
ValueCountFrequency (%)
M711
97.8%
L8
 
1.1%
T8
 
1.1%
ValueCountFrequency (%)
.698
97.1%
,21
 
2.9%
ValueCountFrequency (%)
$719
100.0%
ValueCountFrequency (%)
-711
100.0%
ValueCountFrequency (%)
727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5109
50.3%
Latin5041
49.7%

Most frequent character per script

ValueCountFrequency (%)
i1422
28.2%
l1422
28.2%
n719
14.3%
M711
14.1%
o711
14.1%
s16
 
0.3%
L8
 
0.2%
e8
 
0.2%
T8
 
0.2%
h8
 
0.2%
ValueCountFrequency (%)
51047
20.5%
727
14.2%
$719
14.1%
-711
13.9%
2698
13.7%
.698
13.7%
1383
 
7.5%
0105
 
2.1%
,21
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10150
100.0%

Most frequent character per block

ValueCountFrequency (%)
i1422
14.0%
l1422
14.0%
51047
10.3%
727
7.2%
$719
7.1%
n719
7.1%
-711
7.0%
M711
7.0%
o711
7.0%
2698
6.9%
Other values (10)1263
12.4%

Management/Administration Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$10,000 to $25,000
560 
$25,000 to $50,000
89 
$5,000 to $10,000
57 
$2,500 to $5,000
 
7
Less than $2,500
 
6

Length

Max length18
Median length18
Mean length17.88456189
Min length16

Characters and Unicode

Total characters12859
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$10,000 to $25,000
4th row$10,000 to $25,000
5th row$10,000 to $25,000
ValueCountFrequency (%)
$10,000 to $25,000560
77.8%
$25,000 to $50,00089
 
12.4%
$5,000 to $10,00057
 
7.9%
$2,500 to $5,0007
 
1.0%
Less than $2,5006
 
0.8%
(Missing)1
 
0.1%
2021-01-21T00:51:42.509968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:42.961037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to713
33.1%
25,000649
30.1%
10,000617
28.6%
50,00089
 
4.1%
5,00064
 
3.0%
2,50013
 
0.6%
less6
 
0.3%
than6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
04989
38.8%
1438
 
11.2%
$1432
 
11.1%
,1432
 
11.1%
5815
 
6.3%
t719
 
5.6%
o713
 
5.5%
2662
 
5.1%
1617
 
4.8%
s12
 
0.1%
Other values (5)30
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7083
55.1%
Lowercase Letter1468
 
11.4%
Space Separator1438
 
11.2%
Currency Symbol1432
 
11.1%
Other Punctuation1432
 
11.1%
Uppercase Letter6
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t719
49.0%
o713
48.6%
s12
 
0.8%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
04989
70.4%
5815
 
11.5%
2662
 
9.3%
1617
 
8.7%
ValueCountFrequency (%)
$1432
100.0%
ValueCountFrequency (%)
,1432
100.0%
ValueCountFrequency (%)
1438
100.0%
ValueCountFrequency (%)
L6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11385
88.5%
Latin1474
 
11.5%

Most frequent character per script

ValueCountFrequency (%)
t719
48.8%
o713
48.4%
s12
 
0.8%
L6
 
0.4%
e6
 
0.4%
h6
 
0.4%
a6
 
0.4%
n6
 
0.4%
ValueCountFrequency (%)
04989
43.8%
1438
 
12.6%
$1432
 
12.6%
,1432
 
12.6%
5815
 
7.2%
2662
 
5.8%
1617
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12859
100.0%

Most frequent character per block

ValueCountFrequency (%)
04989
38.8%
1438
 
11.2%
$1432
 
11.1%
,1432
 
11.1%
5815
 
6.3%
t719
 
5.6%
o713
 
5.5%
2662
 
5.1%
1617
 
4.8%
s12
 
0.1%
Other values (5)30
 
0.2%

Office Supplies Expense
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$20,000 to $50,000
381 
$50,000 to $100,000
317 
$10,000 to $20,000
 
13
$5,000 to $10,000
 
7
Less than $5,000
 
1

Length

Max length19
Median length18
Mean length18.42837274
Min length16

Characters and Unicode

Total characters13250
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row$20,000 to $50,000
2nd row$50,000 to $100,000
3rd row$50,000 to $100,000
4th row$20,000 to $50,000
5th row$50,000 to $100,000
ValueCountFrequency (%)
$20,000 to $50,000381
52.9%
$50,000 to $100,000317
44.0%
$10,000 to $20,00013
 
1.8%
$5,000 to $10,0007
 
1.0%
Less than $5,0001
 
0.1%
(Missing)1
 
0.1%
2021-01-21T00:51:43.510596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:43.665388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to718
33.3%
50,000698
32.4%
20,000394
18.3%
100,000317
14.7%
10,00020
 
0.9%
5,0008
 
0.4%
less1
 
< 0.1%
than1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
06057
45.7%
1438
 
10.9%
$1437
 
10.8%
,1437
 
10.8%
t719
 
5.4%
o718
 
5.4%
5706
 
5.3%
2394
 
3.0%
1337
 
2.5%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7494
56.6%
Lowercase Letter1443
 
10.9%
Space Separator1438
 
10.9%
Currency Symbol1437
 
10.8%
Other Punctuation1437
 
10.8%
Uppercase Letter1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t719
49.8%
o718
49.8%
s2
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
06057
80.8%
5706
 
9.4%
2394
 
5.3%
1337
 
4.5%
ValueCountFrequency (%)
$1437
100.0%
ValueCountFrequency (%)
,1437
100.0%
ValueCountFrequency (%)
1438
100.0%
ValueCountFrequency (%)
L1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11806
89.1%
Latin1444
 
10.9%

Most frequent character per script

ValueCountFrequency (%)
t719
49.8%
o718
49.7%
s2
 
0.1%
L1
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
06057
51.3%
1438
 
12.2%
$1437
 
12.2%
,1437
 
12.2%
5706
 
6.0%
2394
 
3.3%
1337
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII13250
100.0%

Most frequent character per block

ValueCountFrequency (%)
06057
45.7%
1438
 
10.9%
$1437
 
10.8%
,1437
 
10.8%
t719
 
5.4%
o718
 
5.4%
5706
 
5.3%
2394
 
3.0%
1337
 
2.5%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Own or Lease
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.9%
Missing375
Missing (%)52.1%
Memory size5.8 KiB
Unknown
127 
Own
126 
Lease
92 

Length

Max length7
Median length5
Mean length5.005797101
Min length3

Characters and Unicode

Total characters1727
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn
2nd rowUnknown
3rd rowOwn
4th rowOwn
5th rowOwn
ValueCountFrequency (%)
Unknown127
 
17.6%
Own126
 
17.5%
Lease92
 
12.8%
(Missing)375
52.1%
2021-01-21T00:51:44.288544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:44.439172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
unknown127
36.8%
own126
36.5%
lease92
26.7%

Most occurring characters

ValueCountFrequency (%)
n507
29.4%
w253
14.6%
e184
 
10.7%
U127
 
7.4%
k127
 
7.4%
o127
 
7.4%
O126
 
7.3%
L92
 
5.3%
a92
 
5.3%
s92
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1382
80.0%
Uppercase Letter345
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
n507
36.7%
w253
18.3%
e184
 
13.3%
k127
 
9.2%
o127
 
9.2%
a92
 
6.7%
s92
 
6.7%
ValueCountFrequency (%)
U127
36.8%
O126
36.5%
L92
26.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1727
100.0%

Most frequent character per script

ValueCountFrequency (%)
n507
29.4%
w253
14.6%
e184
 
10.7%
U127
 
7.4%
k127
 
7.4%
o127
 
7.4%
O126
 
7.3%
L92
 
5.3%
a92
 
5.3%
s92
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1727
100.0%

Most frequent character per block

ValueCountFrequency (%)
n507
29.4%
w253
14.6%
e184
 
10.7%
U127
 
7.4%
k127
 
7.4%
o127
 
7.4%
O126
 
7.3%
L92
 
5.3%
a92
 
5.3%
s92
 
5.3%

Package Container Expense
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$10,000 to $25,000
450 
$25,000 to $50,000
242 
$5,000 to $10,000
 
19
$1,000 to $5,000
 
8

Length

Max length18
Median length18
Mean length17.95132128
Min length16

Characters and Unicode

Total characters12907
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$10,000 to $25,000
4th row$10,000 to $25,000
5th row$25,000 to $50,000
ValueCountFrequency (%)
$10,000 to $25,000450
62.5%
$25,000 to $50,000242
33.6%
$5,000 to $10,00019
 
2.6%
$1,000 to $5,0008
 
1.1%
(Missing)1
 
0.1%
2021-01-21T00:51:44.825550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:44.985005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to719
33.3%
25,000692
32.1%
10,000469
21.7%
50,000242
 
11.2%
5,00027
 
1.3%
1,0008
 
0.4%

Most occurring characters

ValueCountFrequency (%)
05025
38.9%
$1438
 
11.1%
,1438
 
11.1%
1438
 
11.1%
5961
 
7.4%
t719
 
5.6%
o719
 
5.6%
2692
 
5.4%
1477
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7155
55.4%
Currency Symbol1438
 
11.1%
Other Punctuation1438
 
11.1%
Space Separator1438
 
11.1%
Lowercase Letter1438
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
05025
70.2%
5961
 
13.4%
2692
 
9.7%
1477
 
6.7%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%
ValueCountFrequency (%)
$1438
100.0%
ValueCountFrequency (%)
,1438
100.0%
ValueCountFrequency (%)
1438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11469
88.9%
Latin1438
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
05025
43.8%
$1438
 
12.5%
,1438
 
12.5%
1438
 
12.5%
5961
 
8.4%
2692
 
6.0%
1477
 
4.2%
ValueCountFrequency (%)
t719
50.0%
o719
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12907
100.0%

Most frequent character per block

ValueCountFrequency (%)
05025
38.9%
$1438
 
11.1%
,1438
 
11.1%
1438
 
11.1%
5961
 
7.4%
t719
 
5.6%
o719
 
5.6%
2692
 
5.4%
1477
 
3.7%

Payroll and Benefits Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$500,000 to $1 Million
484 
$250,000 to $500,000
114 
$1 to $2.5 Million
106 
$100,000 to $250,000
 
9
Less than $100,000
 
6

Length

Max length22
Median length22
Mean length21.03477051
Min length18

Characters and Unicode

Total characters15124
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$250,000 to $500,000
2nd row$500,000 to $1 Million
3rd row$500,000 to $1 Million
4th row$500,000 to $1 Million
5th row$500,000 to $1 Million
ValueCountFrequency (%)
$500,000 to $1 Million484
67.2%
$250,000 to $500,000114
 
15.8%
$1 to $2.5 Million106
 
14.7%
$100,000 to $250,0009
 
1.2%
Less than $100,0006
 
0.8%
(Missing)1
 
0.1%
2021-01-21T00:51:45.500360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:45.655505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to713
26.0%
500,000598
21.8%
million590
21.5%
1590
21.5%
250,000123
 
4.5%
2.5106
 
3.9%
100,00015
 
0.5%
than6
 
0.2%
less6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
03557
23.5%
2028
13.4%
$1432
9.5%
o1303
 
8.6%
i1180
 
7.8%
l1180
 
7.8%
5827
 
5.5%
,736
 
4.9%
t719
 
4.8%
1605
 
4.0%
Other values (9)1557
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5218
34.5%
Lowercase Letter5008
33.1%
Space Separator2028
 
13.4%
Currency Symbol1432
 
9.5%
Other Punctuation842
 
5.6%
Uppercase Letter596
 
3.9%

Most frequent character per category

ValueCountFrequency (%)
o1303
26.0%
i1180
23.6%
l1180
23.6%
t719
14.4%
n596
11.9%
s12
 
0.2%
e6
 
0.1%
h6
 
0.1%
a6
 
0.1%
ValueCountFrequency (%)
03557
68.2%
5827
 
15.8%
1605
 
11.6%
2229
 
4.4%
ValueCountFrequency (%)
,736
87.4%
.106
 
12.6%
ValueCountFrequency (%)
M590
99.0%
L6
 
1.0%
ValueCountFrequency (%)
$1432
100.0%
ValueCountFrequency (%)
2028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9520
62.9%
Latin5604
37.1%

Most frequent character per script

ValueCountFrequency (%)
o1303
23.3%
i1180
21.1%
l1180
21.1%
t719
12.8%
n596
10.6%
M590
10.5%
s12
 
0.2%
L6
 
0.1%
e6
 
0.1%
h6
 
0.1%
ValueCountFrequency (%)
03557
37.4%
2028
21.3%
$1432
15.0%
5827
 
8.7%
,736
 
7.7%
1605
 
6.4%
2229
 
2.4%
.106
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15124
100.0%

Most frequent character per block

ValueCountFrequency (%)
03557
23.5%
2028
13.4%
$1432
9.5%
o1303
 
8.6%
i1180
 
7.8%
l1180
 
7.8%
5827
 
5.5%
,736
 
4.9%
t719
 
4.8%
1605
 
4.0%
Other values (9)1557
10.3%

Purchase Print Expenses
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$1,000 to $2,500
357 
$2,500 to $5,000
340 
$500 to $1,000
 
13
Less than $500
 
8

Length

Max length16
Median length16
Mean length15.94150418
Min length14

Characters and Unicode

Total characters11446
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $2,500
2nd row$2,500 to $5,000
3rd row$2,500 to $5,000
4th row$1,000 to $2,500
5th row$2,500 to $5,000
ValueCountFrequency (%)
$1,000 to $2,500357
49.6%
$2,500 to $5,000340
47.2%
$500 to $1,00013
 
1.8%
Less than $5008
 
1.1%
(Missing)2
 
0.3%
2021-01-21T00:51:46.210041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:46.366539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to710
33.0%
2,500697
32.4%
1,000370
17.2%
5,000340
15.8%
50021
 
1.0%
less8
 
0.4%
than8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
03566
31.2%
1436
12.5%
$1428
12.5%
,1407
 
12.3%
51058
 
9.2%
t718
 
6.3%
o710
 
6.2%
2697
 
6.1%
1370
 
3.2%
s16
 
0.1%
Other values (5)40
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5691
49.7%
Lowercase Letter1476
 
12.9%
Space Separator1436
 
12.5%
Currency Symbol1428
 
12.5%
Other Punctuation1407
 
12.3%
Uppercase Letter8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t718
48.6%
o710
48.1%
s16
 
1.1%
e8
 
0.5%
h8
 
0.5%
a8
 
0.5%
n8
 
0.5%
ValueCountFrequency (%)
03566
62.7%
51058
 
18.6%
2697
 
12.2%
1370
 
6.5%
ValueCountFrequency (%)
$1428
100.0%
ValueCountFrequency (%)
,1407
100.0%
ValueCountFrequency (%)
1436
100.0%
ValueCountFrequency (%)
L8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9962
87.0%
Latin1484
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
t718
48.4%
o710
47.8%
s16
 
1.1%
L8
 
0.5%
e8
 
0.5%
h8
 
0.5%
a8
 
0.5%
n8
 
0.5%
ValueCountFrequency (%)
03566
35.8%
1436
14.4%
$1428
14.3%
,1407
 
14.1%
51058
 
10.6%
2697
 
7.0%
1370
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII11446
100.0%

Most frequent character per block

ValueCountFrequency (%)
03566
31.2%
1436
12.5%
$1428
12.5%
,1407
 
12.3%
51058
 
9.2%
t718
 
6.3%
o710
 
6.2%
2697
 
6.1%
1370
 
3.2%
s16
 
0.1%
Other values (5)40
 
0.3%

Rent Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$10,000 to $25,000
324 
$100,000 to $250,000
229 
$50,000 to $100,000
58 
Less than $10,000
56 
$25,000 to $50,000
52 

Length

Max length20
Median length18
Mean length18.63977747
Min length17

Characters and Unicode

Total characters13402
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$100,000 to $250,000
4th row$10,000 to $25,000
5th row$10,000 to $25,000
ValueCountFrequency (%)
$10,000 to $25,000324
45.0%
$100,000 to $250,000229
31.8%
$50,000 to $100,00058
 
8.1%
Less than $10,00056
 
7.8%
$25,000 to $50,00052
 
7.2%
(Missing)1
 
0.1%
2021-01-21T00:51:46.831415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:46.984115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to663
30.7%
10,000380
17.6%
25,000376
17.4%
100,000287
13.3%
250,000229
 
10.6%
50,000110
 
5.1%
less56
 
2.6%
than56
 
2.6%

Most occurring characters

ValueCountFrequency (%)
05439
40.6%
1438
 
10.7%
$1382
 
10.3%
,1382
 
10.3%
t719
 
5.4%
5715
 
5.3%
1667
 
5.0%
o663
 
4.9%
2605
 
4.5%
s112
 
0.8%
Other values (5)280
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7426
55.4%
Lowercase Letter1718
 
12.8%
Space Separator1438
 
10.7%
Currency Symbol1382
 
10.3%
Other Punctuation1382
 
10.3%
Uppercase Letter56
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
t719
41.9%
o663
38.6%
s112
 
6.5%
e56
 
3.3%
h56
 
3.3%
a56
 
3.3%
n56
 
3.3%
ValueCountFrequency (%)
05439
73.2%
5715
 
9.6%
1667
 
9.0%
2605
 
8.1%
ValueCountFrequency (%)
$1382
100.0%
ValueCountFrequency (%)
,1382
100.0%
ValueCountFrequency (%)
1438
100.0%
ValueCountFrequency (%)
L56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11628
86.8%
Latin1774
 
13.2%

Most frequent character per script

ValueCountFrequency (%)
t719
40.5%
o663
37.4%
s112
 
6.3%
L56
 
3.2%
e56
 
3.2%
h56
 
3.2%
a56
 
3.2%
n56
 
3.2%
ValueCountFrequency (%)
05439
46.8%
1438
 
12.4%
$1382
 
11.9%
,1382
 
11.9%
5715
 
6.1%
1667
 
5.7%
2605
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII13402
100.0%

Most frequent character per block

ValueCountFrequency (%)
05439
40.6%
1438
 
10.7%
$1382
 
10.3%
,1382
 
10.3%
t719
 
5.4%
5715
 
5.3%
1667
 
5.0%
o663
 
4.9%
2605
 
4.5%
s112
 
0.8%
Other values (5)280
 
2.1%

Square Footage
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2,500 - 4,999
690 
1 - 1,499
 
26
5,000 - 9,999
 
2
1,500 - 2,499
 
1
10,000 - 19,999
 
1

Length

Max length15
Median length13
Mean length12.85833333
Min length9

Characters and Unicode

Total characters9258
Distinct characters9
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row2,500 - 4,999
2nd row2,500 - 4,999
3rd row2,500 - 4,999
4th row2,500 - 4,999
5th row2,500 - 4,999
ValueCountFrequency (%)
2,500 - 4,999690
95.8%
1 - 1,49926
 
3.6%
5,000 - 9,9992
 
0.3%
1,500 - 2,4991
 
0.1%
10,000 - 19,9991
 
0.1%
2021-01-21T00:51:47.553328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:47.699783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
720
33.3%
4,999690
31.9%
2,500690
31.9%
1,49926
 
1.2%
126
 
1.2%
9,9992
 
0.1%
5,0002
 
0.1%
1,5001
 
< 0.1%
10,0001
 
< 0.1%
19,9991
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
92136
23.1%
1440
15.6%
,1414
15.3%
01392
15.0%
-720
 
7.8%
4717
 
7.7%
5693
 
7.5%
2691
 
7.5%
155
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5684
61.4%
Space Separator1440
 
15.6%
Other Punctuation1414
 
15.3%
Dash Punctuation720
 
7.8%

Most frequent character per category

ValueCountFrequency (%)
92136
37.6%
01392
24.5%
4717
 
12.6%
5693
 
12.2%
2691
 
12.2%
155
 
1.0%
ValueCountFrequency (%)
,1414
100.0%
ValueCountFrequency (%)
1440
100.0%
ValueCountFrequency (%)
-720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9258
100.0%

Most frequent character per script

ValueCountFrequency (%)
92136
23.1%
1440
15.6%
,1414
15.3%
01392
15.0%
-720
 
7.8%
4717
 
7.7%
5693
 
7.5%
2691
 
7.5%
155
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII9258
100.0%

Most frequent character per block

ValueCountFrequency (%)
92136
23.1%
1440
15.6%
,1414
15.3%
01392
15.0%
-720
 
7.8%
4717
 
7.7%
5693
 
7.5%
2691
 
7.5%
155
 
0.6%

Telcom Expenses
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$5,000 to $20,000
599 
$2,000 to $5,000
108 
Less than $2,000
 
12

Length

Max length17
Median length17
Mean length16.83310153
Min length16

Characters and Unicode

Total characters12103
Distinct characters14
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,000 to $5,000
2nd row$5,000 to $20,000
3rd row$5,000 to $20,000
4th row$5,000 to $20,000
5th row$5,000 to $20,000
ValueCountFrequency (%)
$5,000 to $20,000599
83.2%
$2,000 to $5,000108
 
15.0%
Less than $2,00012
 
1.7%
(Missing)1
 
0.1%
2021-01-21T00:51:48.110372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:48.250190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to707
32.8%
5,000707
32.8%
20,000599
27.8%
2,000120
 
5.6%
less12
 
0.6%
than12
 
0.6%

Most occurring characters

ValueCountFrequency (%)
04877
40.3%
1438
 
11.9%
$1426
 
11.8%
,1426
 
11.8%
2719
 
5.9%
t719
 
5.9%
o707
 
5.8%
5707
 
5.8%
s24
 
0.2%
L12
 
0.1%
Other values (4)48
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6303
52.1%
Lowercase Letter1498
 
12.4%
Space Separator1438
 
11.9%
Currency Symbol1426
 
11.8%
Other Punctuation1426
 
11.8%
Uppercase Letter12
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t719
48.0%
o707
47.2%
s24
 
1.6%
e12
 
0.8%
h12
 
0.8%
a12
 
0.8%
n12
 
0.8%
ValueCountFrequency (%)
04877
77.4%
2719
 
11.4%
5707
 
11.2%
ValueCountFrequency (%)
$1426
100.0%
ValueCountFrequency (%)
,1426
100.0%
ValueCountFrequency (%)
1438
100.0%
ValueCountFrequency (%)
L12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10593
87.5%
Latin1510
 
12.5%

Most frequent character per script

ValueCountFrequency (%)
t719
47.6%
o707
46.8%
s24
 
1.6%
L12
 
0.8%
e12
 
0.8%
h12
 
0.8%
a12
 
0.8%
n12
 
0.8%
ValueCountFrequency (%)
04877
46.0%
1438
 
13.6%
$1426
 
13.5%
,1426
 
13.5%
2719
 
6.8%
5707
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII12103
100.0%

Most frequent character per block

ValueCountFrequency (%)
04877
40.3%
1438
 
11.9%
$1426
 
11.8%
,1426
 
11.8%
2719
 
5.9%
t719
 
5.9%
o707
 
5.8%
5707
 
5.8%
s24
 
0.2%
L12
 
0.1%
Other values (4)48
 
0.4%

Utilities Expenses
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)1.0%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
$2,000 to $5,000
315 
$50,000 to $100,000
178 
$5,000 to $10,000
61 
Over $100,000
52 
$25,000 to $50,000
42 
Other values (2)
71 

Length

Max length19
Median length16
Mean length16.81641168
Min length13

Characters and Unicode

Total characters12091
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,000 to $5,000
2nd row$2,000 to $5,000
3rd row$50,000 to $100,000
4th row$2,000 to $5,000
5th row$2,000 to $5,000
ValueCountFrequency (%)
$2,000 to $5,000315
43.8%
$50,000 to $100,000178
24.7%
$5,000 to $10,00061
 
8.5%
Over $100,00052
 
7.2%
$25,000 to $50,00042
 
5.8%
Less than $2,00039
 
5.4%
$10,000 to $25,00032
 
4.4%
(Missing)1
 
0.1%
2021-01-21T00:51:48.685395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:51:48.816591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to628
29.8%
5,000376
17.9%
2,000354
16.8%
100,000230
 
10.9%
50,000220
 
10.5%
10,00093
 
4.4%
25,00074
 
3.5%
over52
 
2.5%
less39
 
1.9%
than39
 
1.9%

Most occurring characters

ValueCountFrequency (%)
04814
39.8%
1386
 
11.5%
$1347
 
11.1%
,1347
 
11.1%
5670
 
5.5%
t667
 
5.5%
o628
 
5.2%
2428
 
3.5%
1323
 
2.7%
e91
 
0.8%
Other values (8)390
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6235
51.6%
Lowercase Letter1685
 
13.9%
Space Separator1386
 
11.5%
Currency Symbol1347
 
11.1%
Other Punctuation1347
 
11.1%
Uppercase Letter91
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
t667
39.6%
o628
37.3%
e91
 
5.4%
s78
 
4.6%
v52
 
3.1%
r52
 
3.1%
h39
 
2.3%
a39
 
2.3%
n39
 
2.3%
ValueCountFrequency (%)
04814
77.2%
5670
 
10.7%
2428
 
6.9%
1323
 
5.2%
ValueCountFrequency (%)
O52
57.1%
L39
42.9%
ValueCountFrequency (%)
$1347
100.0%
ValueCountFrequency (%)
,1347
100.0%
ValueCountFrequency (%)
1386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10315
85.3%
Latin1776
 
14.7%

Most frequent character per script

ValueCountFrequency (%)
t667
37.6%
o628
35.4%
e91
 
5.1%
s78
 
4.4%
O52
 
2.9%
v52
 
2.9%
r52
 
2.9%
L39
 
2.2%
h39
 
2.2%
a39
 
2.2%
ValueCountFrequency (%)
04814
46.7%
1386
 
13.4%
$1347
 
13.1%
,1347
 
13.1%
5670
 
6.5%
2428
 
4.1%
1323
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12091
100.0%

Most frequent character per block

ValueCountFrequency (%)
04814
39.8%
1386
 
11.5%
$1347
 
11.1%
,1347
 
11.1%
5670
 
5.5%
t667
 
5.5%
o628
 
5.2%
2428
 
3.5%
1323
 
2.7%
e91
 
0.8%
Other values (8)390
 
3.2%

Interactions

2021-01-21T00:51:13.899338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:14.149372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:14.375873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:14.542151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:14.802583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:15.043908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:15.292259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:15.520196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:15.730459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:15.890303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:16.118133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:16.335111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:16.566133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:16.790771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:16.997678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:17.155699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:17.380149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:17.596146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:17.836945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:18.194010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:18.354143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:18.508699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:18.675025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:18.822917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:18.984156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:19.236345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:19.467972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:19.690017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:19.864410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:20.100574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:20.353635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:20.583502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:20.803029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:21.017147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:21.189226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:21.420036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:21.655982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:21.904236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:22.144107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:22.374426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:22.540685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:51:22.793209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-21T00:51:49.175690image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-21T00:51:49.476782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-21T00:51:49.763477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-21T00:51:50.140534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-21T00:51:50.816318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-21T00:51:23.432562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-21T00:51:25.819057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-21T00:51:26.825411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-21T00:51:27.943681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexAdvertising ExpensesAccounting ExpensesAddressCityStateCountyMetro AreaNeighborhoodZIP CodeZIP FourYear EstablishedYears In DatabaseCompany NameComputer ExpensesContract Labor ExpensesCorporate Sales Volume ActualInsurance ExpensesLast Updated OnLegal ExpensesLocation Employee Size ActualLocation Employee Size RangeLocation Sales Volume ActualLocation Sales Volume RangeManagement/Administration ExpensesOffice Supplies ExpenseOwn or LeasePackage Container ExpensePayroll and Benefits ExpensesPurchase Print ExpensesRent ExpensesSquare FootageTelcom ExpensesUtilities Expenses
00$20,000 to $50,000$2,500 to $5,000265 W Oakland Park BlvdWilton ManorsFLBrowardMiami-Ft Ldr, FLSleepy River333111707.0NaN37Mc Donald's$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202010$1,000 to $2,5003020 to 49$1,564,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
11$50,000 to $100,000$5,000 to $10,000326 Indian TrceWestonFLBrowardMiami-Ft Ldr, FLWeston333262996.0NaN24Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004920 to 49$2,554,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Unknown$10,000 to $25,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
22$50,000 to $100,000$5,000 to $10,0001020 Weston RdWestonFLBrowardMiami-Ft Ldr, FLNaN333261917.0NaN32Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0005050 to 99$2,606,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Own$10,000 to $25,000$500,000 to $1 Million$2,500 to $5,000$100,000 to $250,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000
33$50,000 to $100,000$5,000 to $10,0009835 Okeechobee BlvdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLBaywinds334111833.0NaN13Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,372,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
44$50,000 to $100,000$5,000 to $10,000828 S Military TrlWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334153908.0NaN28Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0005650 to 99$2,952,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Own$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
55$50,000 to $100,000$5,000 to $10,0006858 Okeechobee BlvdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLGolden Lakes334112510.0NaN30Mc Donald's$5,000 to $10,000$10,000 to $50,000$0$50,000 to $100,000202010$5,000 to $10,0006550 to 99$3,426,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Own$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
66$50,000 to $100,000$5,000 to $10,000650 Belvedere RdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334051231.0NaN36Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004020 to 49$2,109,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$100,000 to $250,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000
77$20,000 to $50,000$5,000 to $10,0004275 45th StWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334071859.0NaN30Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0003520 to 49$1,845,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
88$50,000 to $100,000$5,000 to $10,0003015 Forest Hill BlvdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334065908.0NaN4Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,372,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000Less than $2,000
99$50,000 to $100,000$5,000 to $10,0002605 S Military TrlWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLShoppes At Cresthaven334157549.0NaN9Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,372,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000

Last rows

df_indexAdvertising ExpensesAccounting ExpensesAddressCityStateCountyMetro AreaNeighborhoodZIP CodeZIP FourYear EstablishedYears In DatabaseCompany NameComputer ExpensesContract Labor ExpensesCorporate Sales Volume ActualInsurance ExpensesLast Updated OnLegal ExpensesLocation Employee Size ActualLocation Employee Size RangeLocation Sales Volume ActualLocation Sales Volume RangeManagement/Administration ExpensesOffice Supplies ExpenseOwn or LeasePackage Container ExpensePayroll and Benefits ExpensesPurchase Print ExpensesRent ExpensesSquare FootageTelcom ExpensesUtilities Expenses
710714$20,000 to $50,000$2,500 to $5,0004174 White Plains RdBronxNYBronxNw Yrk, NY-NJ-PAWakefield104663012.0NaN25Mc Donald's$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202010$1,000 to $2,5002020 to 49$930,000$500,000-1 Million$5,000 to $10,000$10,000 to $20,000Own$5,000 to $10,000$250,000 to $500,000$500 to $1,000$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
711715$50,000 to $100,000$5,000 to $10,00051-67 161st StBronxNYBronxNw Yrk, NY-NJ-PANaN10451NaNNaN29Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0005950 to 99$2,742,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Unknown$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
712716$50,000 to $100,000$5,000 to $10,0005765 BroadwayBronxNYBronxNw Yrk, NY-NJ-PAKingsbridge104634144.0NaN37Mc Donald's$2,500 to $5,000$10,000 to $50,000$0$25,000 to $50,000202010$2,500 to $5,0006550 to 99$3,021,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Lease$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
713717$20,000 to $50,000$5,000 to $10,000597-99 Grand ConcourseBronxNYBronxNw Yrk, NY-NJ-PANaN10451NaNNaN12Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004020 to 49$1,859,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
714718$20,000 to $50,000$5,000 to $10,000599 E Tremont AveBronxNYBronxNw Yrk, NY-NJ-PAEast Tremont104574727.0NaN14Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004020 to 49$1,859,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
715719$50,000 to $100,000$5,000 to $10,000724 E 241st StBronxNYBronxNw Yrk, NY-NJ-PAWakefield104701302.0NaN20Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,091,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$100,000 to $250,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000
716720$20,000 to $50,000$2,500 to $5,000839 Westchester AveBronxNYBronxNw Yrk, NY-NJ-PAWoodstock104551704.0NaN18Mc Donald's$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202010$1,000 to $2,5003020 to 49$1,394,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500Less than $10,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
717721$50,000 to $100,000$5,000 to $10,00086 E 167th StBronxNYBronxNw Yrk, NY-NJ-PAConcourse104528203.0NaN16Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,091,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000Less than $2,000
718722$20,000 to $50,000$2,500 to $5,000875 Garrison AveBronxNYBronxNw Yrk, NY-NJ-PAHunts Point104745305.0NaN31Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$1,000 to $2,5003520 to 49$1,627,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
719723$50,000 to $100,000$5,000 to $10,000925 Hunts Point AveBronxNYBronxNw Yrk, NY-NJ-PANaN104595190.0NaN4Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,091,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000